UNSUPERVISED SEGMENTATION OF SMALLHOLDER FIELDS IN MOZAMBIQUE USING PLANETSCOPE IMAGERY

Land use, i.e. how human societies manage and interact with land through social-ecological systems, is at the core of sustainability issues. Democratic backsliding, i.e. the decline or degradation of the institutions and social norms that sustain democratic societies, is a widespread and impactful trend. This paper unpacks the two-ways linkages between land use and democratic backsliding, and argues for an agenda to strengthen research to understanding these two-ways interactions.

Despite the recent strong reduction in European forest carbon uptake, a comparison of EU forest carbon losses from different datasets is still lacking. We quantify these carbon sink losses combining new high-resolution Earth Observation estimates of forest disturbance, EO-based bookkeeping models, atmospheric CO2 inversions, and National Greenhouse Gas Inventories (NGHGI). We highlight how EO and NGHGIs can benefit each other in better quantifying C losses in a comprehensive and timely manner.

Climate risks to food systems can extend beyond national borders through international sourcing networks. We examine Canada’s exposure to such risks by combining provincial food-flow data with climate projections to identify where future agroclimatic changes may affect key fruit and vegetable sources, including increasing heat and dry spells in Mexico and the southwestern US. The analysis assesses exposure under recent sourcing patterns, not future shortages, prices, or production losses.

This study examines the rate of permafrost degradation, in the form of the transition from intact well-drained palsa to fully thawed and inundated fen at the Stordalen mire, Abisko, Sweden. Across the 14 hectares of the palsa mire, we demonstrate a 10-fold acceleration of the degradation in 2019–2021 compared to previous periods (1970–2014) which might lead to a pool of 12 × 10³ kg of organic carbon exposed annually for the topsoil (23 cm depth).

Driftwood piles up along Arctic coasts, storing carbon and shaping habitats, but mapping it automatically is difficult. We compared three artificial intelligence models on aerial photos and satellite images, repeating each test many times to capture natural training variation. Image resolution mattered far more than which model was used, and testing only once, as is common, gave misleading results. Our approach offers a more reliable way to choose mapping tools for similar Arctic features.